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Phenology is critical in simulating crop production and hydrology and must be sufficiently robust to respond to varying environments, soils, and management practices. Phenological algorithms typically focus on the air temperature response function and rarely quantify the phenological responses to varying water deficits, particularly for versions of the Environmental Policy Integrated Climate model (EPIC)‐based plant growth component used in many agroecosystem models. Three EPIC‐based plant growth components (Soil Water Assessment Tool [SWAT], Wind Erosion Prediction System [WEPS], and the Unified Plant Growth Model [UPGM]) have been incorporated into the spatially distributed Agricultural Ecosystems Services model [AgES], and only the UPGM includes a phenological response to varying water deficits. These three plant components were used to evaluate the phenological responses of winter wheat (Triticum aestivumL.) to varying water deficits and whether having a water stress factor in UPGM improves the simulation of phenology. A 3‐yr irrigation study and a 4‐yr study across a rainfed landscape were used in the evaluation. Only the UPGM simulated all five of the developmental stagesmeasured. The UPGM was the only component that simulated a phenological response to variable water deficits, resulting in better prediction of phenology. For example, the RMSE (days) and relative error (RE, days) decreased and index of agreement (d) increased in predicting maturity from SWAT (RMSE = 18.4; RE = 9.2; d = 0.34) to WEPS (RMSE = 6.2; RE = 1.0, d = 0.63) to the UPGM (RMSE = 6.1; RE = 0.1; d = 0.70). Incorporating phenological responses to varying water deficits improves the accuracy and robustness of predicting phenology, which is particularly important in spatially distributed agroecosystem models. Core IdeasPhenology is critical in accurately simulating crop production and hydrology.The AgES watershed model evaluated three EPIC‐based plant growth components.Only UPGM was able to simulate phenological responses to varying water deficits.The results promote more robust simulation of phenology in varying environments. Phenology is critical in accurately simulating crop production and hydrology. The AgES watershed model evaluated three EPIC‐based plant growth components. Only UPGM was able to simulate phenological responses to varying water deficits. The results promote more robust simulation of phenology in varying environments.

The semiarid U.S. Great Plains experiences a large variation in crop yields due to variability in rainfall, soil, and other factors. We analyzed (24‐yr) yields from a no‐till rotation of winter wheat (Triticum aestivumL.)–corn (Zea maysL.) or sorghum [Sorghum bicolor(L.) Moench]–fallow at three sites, each with three soil types along a catena in Colorado. We investigated: (i) effects of soil organic carbon (SOC), length of slope (SL), and other soil properties on yields; and (ii) degree in which variability in annual yields are explained with water variables of rainfall during the fallow, vegetative and reproductive stages, and soil water at planting. Wheat and corn/sorghum yields were strongly related to soil properties (R2= 0.84; 0.97, respectively). Wheat yield regression had only SOC as a significant variable, whereas corn/sorghum had SOC and SL. Water factors explained 37 to 73% of annual variability in wheat yields at the site by soil level, 19 to 63% when pooled over soils within sites, and 35 to 40% pooled over all. For corn, the corresponding percentages were 16 to 64%, 26 to 57%, and 40 to 45%, respectively. Fallow rain made the highest contribution to R2in wheat whereas reproductive rain did for corn/sorghum. We conclude SOC is a stand‐in for long‐term effects of water availability on production, but other natural factors greatly influence the annual yield variability. Combining mean yields determined from soil properties with CV of pooled annual yields, we can estimate mean and standard deviation of yields.

Spatial variation of winter wheat (Triticum aestivumL.) phenology across complex terrain has received little attention despite its importance for assessing overall crop status and making key precision management decisions. Temperature is the environmental factor explaining the greatest variation in phenology, and temperature varies across a landscape. Our primary hypothesis was that variability in thermal time (TT) by landscape position could explain the phenological variation, with a secondary hypothesis that spatial patterns of TT and phenology were related to terrain attributes. Four years of phenology and soil and air temperature data were collected from a 100‐ha field in northeast Colorado. Five developmental stages were monitored at 10 landscape sites each year. Terrain attributes were computed using 5‐m‐grid land surface elevation data. Spatial differences in reaching developmental stages across sites varied from 2 to 12 d within a year. Up to 96% of the phenological variation among years was explained when using a general TT model pooling all data. Refining the TT model, however, by using either air or soil TT at specific sites partially supported the primary hypothesis only for the relatively wetter 2004–2005 and 2006–2007 years (regression relationships explained up to 72% of the variation). Similarly, terrain attributes occasionally explained spatial patterns of phenology in the two wetter years, and attributes with the highest explanatory power varied among years and developmental stages. Further research is needed to better understand the controlling environmental factors and predict spatial patterns of wheat phenology for precision management using terrain attributes.

It is common for agricultural system modelers to enhance their models by learning from other models and incorporating the best state‐the‐science into their models. In this study, the CROPGRO plant growth model of Decision Support System for Agrotechnology Transfer (DSSAT v3.5) was linked to the Root Zone Water Quality Model (RZWQM) to provide RZWQM users an option of using CROPGRO. In the hybrid model, RZWQM supplied CROPGRO with daily soil water and N, soil temperature, and potential evapotranspiration (PET), whereas CROPGRO supplied RZWQM with daily water and N uptake and plant growth variables. The RZWQM‐CROPGRO hybrid model was then evaluated against the original CROPGRO‐soybean model using several data sets from the literature. These data sets represented various drought conditions. Results showed that the RZWQM‐CROPGRO hybrid model simulated higher water stress than the original DSSAT‐CROPGRO model because of higher PET simulated by RZWQM, especially under semiarid climate conditions. Therefore, it was necessary to make some adjustments in the hybrid model under dry and windy conditions, e.g., using a different lower limit of plant available water as DSSAT. The hybrid model with a more detailed soil water balance calculation only affected soil water prediction at the top 60‐cm soil profile where soil water was more dynamic. This study demonstrated a successful linkage between RZWQM and CROPGRO, and the RZWQM‐CROPGRO hybrid model provides users with a tool to conduct detailed simulation of crop production in addition to addressing water quality concerns. This study also demonstrated that, when building models from various sources, compatibility of the interacting modules should be ensured.

Agronomists rely increasingly on computers, and more than half of all producers have access to computers. Increasing farm computer ownership has resulted in intensified efforts to transfer new software technologies to producers; however, little is known about how satisfied producers are with computers and the extent to which computers are actually used. We extended our 1996 survey of Great Plains producers to examine producer computer use and satisfaction and discuss potential implications for agricultural software developers. Building on our earlier computer adoption research, we developed ordered logit models for user satisfaction, frequency of computer use, and number of software applications used. Despite using more robust ordered logit models that fit the data well, surprisingly few explanatory variables were significant. Greater computer skill significantly increased user satisfaction and number of software applications used. Greater education also increased user satisfaction and number of software applications used but reduced frequency of computer use. Farming experience showed similar conflicting results as education, i.e., greater number of years farming resulted in significantly increased computer satisfaction but lower frequency of use and number of software applications used. A few other explanatory variables (e.g., farm owner or operator as the primary computer user had a significant positive influence on frequency of use) were important in one of the three ordered logit models, but no consistent relationship between models was found. Generally, greater frequency of use and computer skill increased perceived usefulness of computers by producers. Implications of these results for agricultural software developers are discussed in the paper.